[1]} is mainly designed for solving dense prediction problems (e.g., object detection and semantic segmentation). It uses Spatial Reduction Layer to reduce the computation by decreasing the dimension of \(K\)  and \(V\) . The feature size is reduced by a patch embedding layer at the beginning of each stage. Due to its pyramid structure, the model can generate multi-scale feature maps and can be trained faster with its smaller feature size.
